A bottom-up approach to emulating emotions is expounded in this thesis. This is intended to be useful in research where a phenomenon is to be emulated but the nature of it can not easily be defined. This approach not only advocates emulating the underlying mechanisms that are proposed to give rise to emotion in natural agents, but also advocates applying an open-mind as to what the phenomenon actually is. There is evidence to suggest that neuromodulation is inherently responsible for giving rise to emotions in natural agents and that emotions consequently modulate the behaviour of the agent. The functionality provided by neuromodulation, when applied to agents with self-organising biologically plausible neural networks, is isolated and studied. In research efforts such as this the definition should emerge from the evidence rather than postulate that the definition, derived from limited information, is correct and should be implemented. An implementation of a working definition only tells us that the definition can be implemented. It does not tell us whether that working definition is itself correct and matches the phenomenon in the real world. If this model of emotions was assumed to be true and implemented in an agent, there would be a danger of precluding implementations that could offer alternative theories as to the relevance of neuromodulation to emotions. By isolating and studying different mechanisms such as neuromodulation that are thought to give rise to emotions, theories can arise as to what emotions are and the functionality that they provide. The application of this approach concludes with a theory as to how some emotions can operate via the use of neuromodulators. The theory is explained using the concepts of dynamical systems, free-energy and entropy.

This thesis investigates the way in which information about the structure of a set of training data with 'natural' characteristics may be used to positively influence the design of associative memory neural network models of the Hopfield type. This is done with a view to reducing the level of connectivity in models of this type. There are three strands to this work. Firstly, an empirical evaluation of the implementation of existing theory is given. Secondly, a number of existing theories are combined to produce novel network models and training regimes. Thirdly, new strategies for constructing and training associative memories based on knowledge of the structure of the training data are proposed. The first conclusion of this work is that, under certain circumstances, performance benefits may be gained by establishing the connectivity in a non-random fashion, guided by the knowledge gained from the structure of the training data. These performance improvements exist in relation to networks in which sparse connectivity is established in a purely random manner. This dilution occurs prior to the training of the network. Secondly, it is verified that, as predicted by existing theory, targeted post-training dilution of network connectivity provides greater performance when compared with networks in which connections are removed at random. Finally, an existing tool for the analysis of the attractor performance of neural networks of this type has been modified and improved. Furthermore, a novel, comprehensive performance analysis tool is proposed.

No description available.

No description available.

Automated site preparation on the Moon using a group of autonomous rovers is a topic of great interest for the establishment of a lunar base. A potentially very useful system in which multiple, autonomous rovers clear soil to create a landing pad while simultaneously forming berms with the soil cleared will be described. An Artificial Neural Tissue (ANT) architecture was used as the control algorithm to accomplish these tasks. This scalable architecture encourages task decomposition of the main mission tasks and requires minimal human supervision. To solve these tasks, a single fitness function to measure the performance of the controller and a set of allowable basis behaviors was defined. Next, an evolutionary (Darwinian) selection process was used to generate controllers in simulation. The fittest controller was subsequently implemented on LEGO robots for additional validation and testing. The ANT controller was ultimately integrated with a team of three large-scale rovers.

Automated site preparation on the Moon using a group of autonomous rovers is a topic of great interest for the establishment of a lunar base. A potentially very useful system in which multiple, autonomous rovers clear soil to create a landing pad while simultaneously forming berms with the soil cleared will be described. An Artificial Neural Tissue (ANT) architecture was used as the control algorithm to accomplish these tasks. This scalable architecture encourages task decomposition of the main mission tasks and requires minimal human supervision. To solve these tasks, a single fitness function to measure the performance of the controller and a set of allowable basis behaviors was defined. Next, an evolutionary (Darwinian) selection process was used to generate controllers in simulation. The fittest controller was subsequently implemented on LEGO robots for additional validation and testing. The ANT controller was ultimately integrated with a team of three large-scale rovers.

This research investigates the use of conducting polymer coatings on platinum (Pt) electrodes for use in neuroprostheses. Conducting polymers aim to provide an environment conducive to neurite outgrowth and attachment at the electrode sites, producing intimate contact between neural cells and stimulating electrodes. Conducting polymers were electropolymerised onto model Pt electrodes. Conventional polymers polypyrrole (PPy) and poly-3,4-ethylenedioxythiphene (PEDOT) doped with polystyrenesulfonate (PSS) and para-toluenesulfonate (pTS)were investigated. Improvement of material properties was assessed through the layering of polymers with multi-walled carbon nanotubes (MWNTs). The ability to incorporate cell attachment bioactivity into polymers was examined through the doping of PEDOT with anionic laminin peptides DCDPGYIGSR and DEDEDYFQRYLI. Finally, nerve growth factor (NGF), was entrapped in PEDOT during polymerisation and tested for neurite outgrowth bioactivity against the PC12 cell line. Each polymer modification was assessed for electrical performance over multiple reduction-oxidation cycles, conductivity and impedance spectroscopy, mechanical adherence and hardness, and biological response. Scanning electron microscopy was used to visualise film topography and x-ray photon spectroscopy was employed to examine chemical constitution of the polymers. For application of electrode coatings to neural prostheses, optimal bioactive conducting polymer PEDOT/pTS/NGF was deposited on electrode arrays intended for implantation. PC12s were used to assess the bioactivity of NGF functionalised PEDOT when electrode size was micronised. Flexibility of the design was tested by tailoring PEDOT bioactivity for the cloned retinal ganglion cell, RGC-5, differentiated via staurasporine. It was established that PEDOT films had superior electrical and cell growth characteristics, but only PPy was able to benefit from incorporation of MWNTs. Bioactive polymers were produced through inclusion of both laminin peptides and NGF, but the optimum film constitution was found to be PEDOT doped with pTS with NGF entrapped during electrodeposition. Application of this polymer to an implant device was confirmed through positive neurite outgrowth on vision prosthesis electrode arrays. The design was shown to be flexible when tailored for RGC-5s, with differentiation occurring on both PEDOT/pTS and PEDOT/DEDEDYFQRYLI. Conducting polymers demonstrate the potential to improve electrode-cell interactions. Future work will focus on the effect of electrical stimulation and design of bioactive polymers with improved cell attachment properties.

Previous research that has evaluated the effectiveness of personality variables for predicting work performance has predominantly relied on methods designed to detect simple relationships. The research reported in this thesis employed artificial neural networks ??? a method that is capable of capturing complex nonlinear and configural relationships among variables ??? and the findings were compared to those obtained by the more traditional method of linear regression. Six datasets that comprise a range of occupations, personality inventories, and work performance measures were used as the basis of the analyses. A series of studies were conducted to compare the predictive performance of prediction equations that a) were developed using either artificial neural networks or linear regression, and b) differed with respect to the type and number of personality variables that were used as predictors of work performance. Studies 1 and 2 compared the two methods using individual personality variables that assess the broad constructs of the five-factor model of personality. Studies 3 and 4 used combinations of these broad variables as the predictors. Study 5 employed narrow personality variables that assess specific facets of the broad constructs. Additional methodological contributions include the use of a resampling procedure, the use of multiple measures of predictive performance, and the comparison of two procedures for developing neural networks. Across the studies, it was generally found that the neural networks were rarely able to outperform the simpler linear regression equations, and this was attributed to the lack of reliable nonlinearity and configurality in personality-work performance relationships. However, the neural networks were able to outperform linear regression in the few instances where there was some independent evidence of nonlinear or configural relationships. Consequently, although the findings do not support the usefulness of neural networks for specifically improving the effectiveness of personality variables as predictors of work performance, in a broader sense they provide some grounds for optimism for organisational researchers interested in applying this method to investigate and exploit complex relationships among variables.

Title page, table of contents and abstract only. The complete thesis in print form is available from the University of Adelaide Library. / Visual movement is important to most animals that move quickly, and even some that do not. What neural computations do animals use to see visual motion in their natural environment? The visual stimulus used to perform experiments on such questions is critical, and has historically limited the ability to perform experiments asking critical questions about responses to naturalistic moving scenes. The ability to display, at high frame rates, moving natural panoramas and other stimuli distorted to compensate for projection onto a flat screen was important to the experiments described here. I therefore created a software library called the 'Vision Egg' that allows creation of motion stimuli with recent, inexpensive computer hardware, and was used for the experiments described here. Additionally, I developed a mathematical model to determine the quality of motion simulation possible with computer displays. This model was applied to reach an understanding of the 'ghosting' artifact sometimes perceived on such apparent motion displays. Psychophysical experiments on human observers confirmed model predictions and allowed testing of synthetic motion blur for simulation of smooth motion and elimination of the ghosting artifact. I show this synthetic motion blur is optimal in the sense of creating the closest perception possible to that of smooth motion experienced in natural settings. Experiments on humans and flies show that such synthetic 'motion blur' has no effect on motion detection per se. However, ghosting in sampled displays results in information not present in smooth motion at high velocities, permitting inappropriate discrimination of rapidly moving features. I performed experiments measuring the responses of hoverfly wide-field motion detecting neurons (HS cells) in adapted and unadapted states to the velocity of natural scenes. Responses to natural images of varied intrinsic contrast depend little on the choice of image. Artificially reducing contrast, however, does reduce response magnitudes. Finally, the greatest component of response variation to natural scenes is directly related to local structure in the scenes, and could thus be called 'pattern noise.' The large receptive field of HS cells arises from a (non-linear) spatial summation of numerous elementary motion detectors. I measured spatial and temporal contrast sensitivity of small patches in the large receptive field. As predicted from the presence of a frontal optical acute zone, spatial tuning is highest frontally. A sexually dimorphic 'bright zone' in the frontodorsal eye is correlated with enhanced contrast sensitivity and faster temporal tuning in HS cells with receptive fields in this region of male flies. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1125182 / Thesis (Ph.D.) -- University of Adelaide, School of Molecular and Biomedical Science, 2004

Title page, table of contents and abstract only. The complete thesis in print form is available from the University of Adelaide Library. / Visual movement is important to most animals that move quickly, and even some that do not. What neural computations do animals use to see visual motion in their natural environment? The visual stimulus used to perform experiments on such questions is critical, and has historically limited the ability to perform experiments asking critical questions about responses to naturalistic moving scenes. The ability to display, at high frame rates, moving natural panoramas and other stimuli distorted to compensate for projection onto a flat screen was important to the experiments described here. I therefore created a software library called the 'Vision Egg' that allows creation of motion stimuli with recent, inexpensive computer hardware, and was used for the experiments described here. Additionally, I developed a mathematical model to determine the quality of motion simulation possible with computer displays. This model was applied to reach an understanding of the 'ghosting' artifact sometimes perceived on such apparent motion displays. Psychophysical experiments on human observers confirmed model predictions and allowed testing of synthetic motion blur for simulation of smooth motion and elimination of the ghosting artifact. I show this synthetic motion blur is optimal in the sense of creating the closest perception possible to that of smooth motion experienced in natural settings. Experiments on humans and flies show that such synthetic 'motion blur' has no effect on motion detection per se. However, ghosting in sampled displays results in information not present in smooth motion at high velocities, permitting inappropriate discrimination of rapidly moving features. I performed experiments measuring the responses of hoverfly wide-field motion detecting neurons (HS cells) in adapted and unadapted states to the velocity of natural scenes. Responses to natural images of varied intrinsic contrast depend little on the choice of image. Artificially reducing contrast, however, does reduce response magnitudes. Finally, the greatest component of response variation to natural scenes is directly related to local structure in the scenes, and could thus be called 'pattern noise.' The large receptive field of HS cells arises from a (non-linear) spatial summation of numerous elementary motion detectors. I measured spatial and temporal contrast sensitivity of small patches in the large receptive field. As predicted from the presence of a frontal optical acute zone, spatial tuning is highest frontally. A sexually dimorphic 'bright zone' in the frontodorsal eye is correlated with enhanced contrast sensitivity and faster temporal tuning in HS cells with receptive fields in this region of male flies. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1125182 / Thesis (Ph.D.) -- University of Adelaide, School of Molecular and Biomedical Science, 2004